fieldRS R package is out!

fieldRS R package is out!

August 3, 2018

New package published in the scope of the Central Asia Waters (CAWa). fieldRS was designed to support scientists and practitioners in the collection of ground-truth data by helping design field campaigns. It provides tools to identify priority sampling sites, map potential sampling plots and convert those plots into consistent training and validation samples. currently, fieldRS addresses field campaigns related to land cover classification. Future updated will also consider the collection of ground-truth data used in the prediction of continuous variables. A vignette with detailed examples can be found here.

Translation of multi-temporal Normalized Difference Vegetation Index (NDVI) images into single sample plots using fieldRS.

you may also like:

Book “Intro to Spatial Data Analysis” in Print

Book “Intro to Spatial Data Analysis” in Print

Our upcoming book "Introduction to Spatial Data Analysis" is finally in print and delivery is expected around end of July. This book provides a gentle introduction to spatial data analysis using mainly QGIS and also working on the command line (R) with spatial data....

R Package for harmonic modelling of time-series data

R Package for harmonic modelling of time-series data

Sentinel-2 NDVI time-series over the Steigerwald. Left: Original satellite scenes after cloud, cloud shadow and snow maksing. Right: Interpolated time-series using a harmonic modelling. In order to fully exploit the monitoring potential of the satellite systems,...

most recent news:

Field work equipment arrived with Earth Observation design

Field work equipment arrived with Earth Observation design

Our field work jackets and backpacks arrived with Earth Observation design. Now we can conduct our UAV campaigns and are easily recognizable as researcher of the University of Wuerzburg. We are looking forward to some time in the field sampling various landcover...

New researcher Pawel Kluter

New researcher Pawel Kluter

Pawel Kluter joined the Department of Remote Sensing as a Research Associate in November 2020. His main role is the deployment of Data Cubes in cloud environments (Front End / Back End), as well as the development of remote sensing processing routines using Python....